Impact of indexing resting metabolic rate against fat-free mass determined by different body composition models.

OBJECTIVE: To examine the differences arising from indexing resting metabolic rate (RMR) against fat-free mass (FFM) determined using two-, three- and four-compartment body composition models. DESIGN: All RMR and body composition measurements were conducted on the same day for each subject following compliance with premeasurement protocols. SUBJECTS: Data were generated from measurements on 104 males (age 32.1+/-12.1 y (mean+/-s.d.); body mass 81.15+/-12.85 kg; height 179.5+/-6.5 cm; body fat 20.6+/-7.6%). INTERVENTIONS: Body density (BD), total body water (TBW) and bone mineral mass (BMM) were measured by hydrodensitometry, deuterium dilution and dual energy X-ray absorptiometry (DXA), respectively. These measures were used to determine two (hydrodensitometry: BD; hydrometry: TBW)-, three (BD and TBW)- and four- compartment (BD, TBW and BMM) FFM values. DXA also provided three compartment derived FFM values. RMR was measured using open circuit indirect calorimetry. RESULTS: Three (body fat group: lean, moderate, high) x five (body composition determination: hydrodensitometry, hydrometry, three-compartment, DXA, four-compartment) ANOVAs were conducted on FFM and RMR kJ.kg FFM(-1).d(-1). Within-group comparisons revealed that hydrodensitometry and DXA were associated with significant (P<0.001) overestimations and underestimations of FFM and RMR kJ.kg FFM(-1).d(-1), respectively, compared with four-compartment-derived criterion values. A significant interaction (P<0.001) resulted from DXA's greater deviations from criterion values in lean subjects. While hydrometric means were not significantly (P> or =0.68) different from criterion values intraindividual differences were large (FFM: -1.5 to 2.9 kg; RMR: -6.0 to 3.2 kJ.kg FFM(-1).d(-1)). CONCLUSION: The relationship between RMR kJ.kg FFM(-1).d(-1) and exercise status would best be investigated using three (BD, TBW)- or four (BD, TBW, BMM)-compartment body composition models to determine FFM. Other models either significantly underestimate indexed RMR (hydrodensitometry, DXA) or display large intraindividual differences (hydrometry) compared with four-compartment derived criterion values. SPONSORSHIP: Australian Research Council (small grants scheme).

Use of anthropometric variables to predict relative body fat determined by a four-compartment body composition model.

OBJECTIVE: To generate equations for the prediction of percent body fat (% BF) via a four-compartment criterion body composition model from anthropometric variables and age. DESIGN: Multiple regression analyses were used to predict % BF from the best-weighted combinations of independent variables. SUBJECTS: In all 79 healthy males (X+/-s.d.: 35.0+/-12.2 y; 84.24+/-12.53 kg; 179.8+/-6.8 cm) aged 19-59 y were recruited from advertisements placed in a university newsletter and on community centres' noticeboards. INTERVENTIONS: The following measurements were conducted: % BF using a four-compartment (water, bone mineral mass, fat and residual) model and a restricted anthropometric profile (nine skinfolds, five girths and two bone breadths). RESULTS: Stepwise multiple regression selected six (subscapular, biceps, abdominal, thigh, calf and mid-axilla) of the nine skinfold measurements to predict % BF and using the sum of these six produced a quadratic equation with a standard error of estimate (SEE) and R(2) of 2.5% BF and 0.89, respectively. The inclusion of age as a predictor further improved the equation (% BF=-0.00057 x ( summation operator 6SF)(2)+0.298 x summation operator 6SF+0.078 x age - 1.13; SEE=2.2% BF, R(2)=0.91). However, the best equation used only the sum of three skinfold thicknesses (mid-axilla, calf and thigh) and age but also included waist girth and biepicondylar femur breadth as predictors (% BF=-0.00258 x ( summation operator 3SF)(2)+0.558 x summation operator 3SF+0.118 x age+0.282 x waist girth - 2.100 x femur breadth - 2.34; SEE=1.8% BF, R(2)=0.94). Analyses of two age groups, <30 and >/=30 y, demonstrated that for the same % BF, the former exhibited a higher sum of skinfold thicknesses. CONCLUSIONS: Equations were generated for the prediction of % BF via the four-compartment criterion body composition model from anthropometric variables and age. Agewise differences for the sum of skinfold thicknesses may be related to an increase in internal fat for the older subjects.

Predicting the resting metabolic rate of 30-60-year-old Australian males.

OBJECTIVES: This study: (a) generated regression equations for predicting the resting metabolic rate (RMR) of 30-60-y-old Australian males from age, height, mass and fat-free mass (FFM); and (b) cross-validated RMR prediction equations which are currently used in Australia against our measured and predicted values. DESIGN: A power analysis demonstrated that 41 subjects would enable the detection of (alpha=0.05, power=0.80) statistically and physiologically significant differences of 8% between predicted/measured RMRs in this study and those predicted from the equations of other investigators. SUBJECTS: Forty-one males ([X]+/-s.d.:, 44.8+/-8.6 y; 83.50+/-11.32 kg; 179.1+/-5.0 cm) were recruited for this study. INTERVENTIONS: The following variables were measured: skinfold thicknesses; RMR using open circuit indirect calorimetry; and FFM via a four-compartment (fat mass, total body water, bone mineral mass and residual) body composition model. RESULTS: A multiple regression equation using mass, height and age as predictors correlated 0.745 with RMR and the s.e.e. was 509 kJ/day. Inclusion of FFM as a predictor increased both the correlation and the precision of prediction, but there was no difference between FFM via the four-compartment model (r=0.816, s.e.e.=429 kJ/day) and that predicted from skinfold thicknesses (r=0.805, s.e.e.=441 kJ/day). CONCLUSIONS: Cross-validation analyses emphasised that equations need to be generated from a large database for the prediction of the RMR of 30-60-y-old Australian males.

Body composition changes in female bodybuilders during preparation for competition.

OBJECTIVE: To determine anthropometric and body composition changes in female bodybuilders during preparation for competition. DESIGN: There was an attempt to match subjects in the control and experimental groups for height and percentage body fat (%BF) for the initial test of this longitudinal study. SUBJECTS: Five competitive bodybuilders (-X +/- s.d.: 35.3 +/- 5.7 y; 167.3 +/- 3.7 cm; 66.38 +/- 6.30 kg; 18.3 +/- 3.5 %BF) and five athletic females (-X +/- s.d.: 30.9 +/- 13.0 y; 166.9 +/- 3.9 cm; 55.94 +/- 3.59 kg; 19.1 +/- 3.3 %BF) were recruited from advertisements in a bodybuilding newsletter and placed on sports centre noticeboards. INTERVENTIONS: The following measurements were conducted 12 weeks, 6 weeks and 3-5 d before the bodybuilders' competitions: anthropometric profile, body density by underwater weighing, total body water via deuterium dilution and bone mineral mass from a dual-energy X-ray absorptiometry scan. A combination of the last three measurements enabled the %BF to the determined by a four compartment model. RESULTS: A significant (P < or = 0.001) 5.80 kg body mass loss by the bodybuilders as they prepared for competition was primarily due to a reduction in fat mass (FM; -4.42 kg; 76.2%) as opposed to fat-free mass (FFM; -1.38 kg; 23.8%). The decreases in body mass and FM over the final 6 weeks were greater than those over the first 6 weeks. Their %BF decreased (P < 0.001) from 18.3 to 12.7, whereas the values for the control group remained essentially unchanged at 19.1-19.6 %BF. These body composition changes by the bodybuilders were accompanied by a significant decline (P < 0.001) of 25.5 mm (76.3-50.8 mm) in the sum of eight skinfold thicknesses (triceps + subscapular + biceps + iliac crest + supraspinale + abdominal + front thigh + medial calf). CONCLUSIONS: Although the bodybuilders presented with low %BFs at the start of the experiment, they still significantly decreased their body mass during the 12 week preparation for competition and most of this loss was due to a reduction in FM as opposed to FFM.

Predicting the resting metabolic rate of young Australian males.

OBJECTIVES: The aims of this study were: (a) to generate regression equations for predicting the resting metabolic rate (RMR) of 18 to 30-y-old Australian males from age, height, mass and fat-free mass (FFM); and (b) cross-validate RMR prediction equations, which are frequently used in Australia, against our measured and predicted values. DESIGN: A power analysis demonstrated that 38 subjects would enable us to detect (alpha = 0.05, power = 0.80) statistically and physiologically significant differences of 8% between our predicted/measured RMRs and those predicted from the equations of other investigators. SUBJECTS: Thirty-eight males (chi +/- s.d.: 24.3+/-3.3y; 85.04+/-13.82 kg; 180.6+/-8.3 cm) were recruited from advertisements placed in a university newsletter and on community centre noticeboards. INTERVENTIONS: The following measurements were conducted: skinfold thicknesses, RMR using open circuit indirect calorimetry and FFM via a four-compartment (fat mass, total body water, bone mineral mass and residual) body composition model. RESULTS: A multiple regression equation using the easily measured predictors of mass, height and age correlated 0.841 with RMR and the SEE was 521 kJ/day. Inclusion of FFM as a predictor increased both the R and the precision of prediction, but there was virtually no difference between FFM via the four-compartment model (R = 0.893, SEE = 433 kJ/day) and that predicted from skinfold thicknesses (R = 0.886, SEE = 440 kJ/day). The regression equations of Harris & Benedict (1919) and Schofield (1985) all overestimated the mean RMR of our subjects by 518 - 600 kJ/day (P < 0.001) and these errors were relatively constant across the range of measured RMR. The equations of Hayter & Henry (1994) and Piers et al (1997) only produced physiologically significant errors at the lower end of our range of measurement. CONCLUSIONS: Equations need to be generated from a large database for the prediction of the RMR of 18 to 30-y-old Australian males and FFM estimated from the regression of the sum of skinfold thicknesses on FFM via the four compartment body composition model needs to be further explored as an expedient RMR predictor.

Comparison of two hydrodensitometric methods for estimating percent body fat.

This study compared the two following hydrodensitometric methods for estimating percent body fat (%BF): 1) estimation of residual volume (RV) by helium dilution before and after measurement of immersed mass at RV, and 2) determination of immersed mass at a comfortable level of expiration (approximately functional residual capacity) with measurement of the associated gas volume by oxygen dilution. Twelve men [27.9 +/- 7.5 (SD) yr; 79.32 +/- 12.79 kg; 180.5 +/- 9.9 cm] were tested for %BF via both methods on each of two separate visits within 3 days by using a counterbalanced design. The two helium dilution measurements yielded a technical error of measurement of 0.2% BF and an intraclass correlation coefficient of 0.999. Corresponding values for the oxygen dilution method were 0.4% BF and 0.999, respectively. There was no difference (P = 0.80) between the helium dilution (16.9 +/- 9.3% BF) and oxygen dilution (16.9 +/- 9.4% BF) methods, and the individual differences ranged from -0.7 to 0.6% BF. The interclass correlation coefficient between the two methods was 0.999 with a SE of estimate of 0.4% BF. Whereas both methods were precise and reliable and yielded similar results, the oxygen dilution technique was more expedient and was preferred by the subjects because they were not required to exhale to RV.